Multiomics Analysis FAQ

• • How Can Genomic, Transcriptomic, Proteomic, and Metabolomic Data Be Effectively Integrated?

  Integrating multi-omics datasets—including genomic, transcriptomic, proteomic, and metabolomic data—is a complex but increasingly essential task in systems biology. The following outlines a systematic approach and key steps for effective integration:   Define the Research Objective and Biological Question The first step in multi-omics integration is to clearly identify the research goal, which directly influences the integration strategy:   1. Disease Mechanism Elucidation Focuses on analyzing cross-t......

• • What Experimental Approaches Can Be Performed Following Transcriptome Sequencing?

  Transcriptome sequencing (RNA-seq) is a powerful technology for investigating gene expression. The data generated from RNA-seq can be utilized for a wide range of downstream analyses and experimental designs, including:   Differential Gene Expression Analysis This is one of the most commonly conducted analyses. Researchers compare transcriptomes under different conditions or treatments (e.g., disease vs. healthy, or treated vs. control) to identify genes that exhibit significant differences in express......

• • How Long Can Tissue Samples Be Stored at −20°C Prior to Transcriptomic and Metabolomic Analyses?

  For transcriptomic and metabolomic analyses, tissue samples should ideally not be stored at −20°C for longer than six months. Extended storage is recommended at −80°C to better preserve the integrity of RNA and the stability of metabolites.   MtoZ Biolabs, an integrated chromatography and mass spectrometry (MS) services provider. Related Services Integrative Transcriptomics-Metabolomics Analysis Service

• • How Can Transcriptome Sequencing Data Be Submitted to the GEO Database?

  The submission of transcriptome sequencing data to the Gene Expression Omnibus (GEO) involves several essential steps, outlined as follows:   Register a GEO Account Before submitting data, you must register for a GEO account. This can be done by visiting the GEO website and clicking on the "Register" button to create an account.   Create a GEO Series After logging into your account, initiate a new GEO Series to house your transcriptome sequencing data. Click the “Submit” button and select the “New GEO......

• • What Are the Next Steps After Differential Gene Expression Analysis Without KEGG or GO Enrichment in Transcriptome Studies?

  After completing transcriptome sequencing and performing only differential gene expression (DGE) analysis without conducting KEGG or Gene Ontology (GO) enrichment analysis, several subsequent steps can be considered to gain deeper biological insights:   1. Interpret the Biological Significance of Differentially Expressed Genes (DEGs) The first step involves interpreting the biological roles of the identified DEGs. This can be achieved by consulting relevant literature and databases to explore the func......

• • How Should Transcriptome Sequencing Data Be Processed and Analyzed?

  Following transcriptome sequencing, a large volume of transcriptomic data is typically generated. To extract biologically meaningful insights, a series of analytical steps must be conducted, including the following:   1. Quality Control of Sequencing Data Ensuring the accuracy and reliability of sequencing data is the first step. This involves assessing sequencing quality scores, removing low-quality bases, eliminating adapter sequences, and filtering reads containing ambiguous nucleotides.   2. Prepr......

• • How Can Transcriptome Analysis Rapidly Identify Differentially Expressed Genes for qPCR Validation in My Research?

  To efficiently identify differentially expressed genes (DEGs) relevant to a specific research focus for qPCR validation, a systematic workflow involving multiple analytical steps is required. The following strategy outlines a possible approach:   Differential Expression Analysis In the RNA-seq data processing pipeline, computational tools such as DESeq2 and edgeR are commonly used to identify DEGs. This step generates a list of genes exhibiting statistically significant expression differences between ......

• • How to Analyze Data and Generate Figures for Eukaryotic Reference-Based Transcriptome Sequencing?

  The analysis of eukaryotic reference-based transcriptome sequencing (RNA-Seq) data is a multi-step process that includes quality control, sequence alignment, expression quantification, differential expression analysis, functional annotation, and pathway analysis. Below is an overview of the analytical workflow:   Data Analysis Workflow 1. Quality Control Use FastQC to assess raw sequencing data quality, including sequence quality scores, nucleotide composition, and sequence duplication rates.   2. Ada......

• • How Do the V3-4 and V4 Regions of 16S rRNA Differ in Microbial Community Analysis?

  The V3-4 and V4 regions of the 16S rRNA gene exhibit distinct characteristics in microbial community analysis, influencing sequencing depth, taxonomic resolution, and cost-effectiveness.   Sequence Coverage 1. V3-4 Region Spanning the third and fourth variable regions of the 16S rRNA gene, this region is longer than the V4 region and captures a broader range of genetic information, enabling more comprehensive microbial profiling.   2. V4 Region Covering only the fourth variable region, this shorter se......

• • What Do "Reads" Represent in Transcriptome Sequencing?

  In transcriptome sequencing, “reads” are fundamental data units that refer to short sequence fragments derived from DNA or RNA samples using sequencing platforms.   When sequencing a sample (such as a transcriptome, which comprises all RNA molecules in a cell or tissue), modern sequencing technologies do not process an entire long DNA or RNA molecule in a single read. Instead, they utilize a series of complex procedures to break the original biomolecules into smaller fragments and then determine the n......